Proposing a New Conceptual Syndemic Framework for COVID-19 Vaccine Hesitancy: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Discussions regarding syndemics have dominated research in recent years. Vaccine hesitancy has also been propelled to the forefront. In this narrative review, we aim to frame a novel syndemic framework to understand the interaction between vaccine hesitancy, COVID-19, and negative health outcomes. METHODS: A non-systematic electronic search was conducted in PubMed and Google Scholar. Search criteria were limited to articles published between November 2019 and June 2022. Articles related to the COVID-19 syndemic and vaccine hesitancy were included. RESULTS: Our review revealed that the adherence to COVID-19 regulations-although they were effective in preventing COVID-19 transmission, cases, and deaths-created a dynamically unstable 'vicious cycle' between undesirable health, economic, and social outcomes. The "accumulation" of complex stressors decreased individuals' cognitive flexibility and hindered them from making decisions and getting vaccinated. Furthermore, it increased individuals' risk of acquiring COVID-19, losing their employment, increasing poverty, and decreasing healthcare utilization. We illustrated how the amalgamation of sociodemographic and contextual factors associated with COVID-19 might impact people's vaccine decisions, making them more hesitant toward COVID-19 vaccination. Failing to receive vaccinations increases the chances of COVID-19 transmission, hospitalization, and other negative health outcomes. CONCLUSIONS: Understanding the interaction between these factors is essential to provide policymakers with inspiration to set appropriate interventions for promoting COVID-19 vaccination acceptance to decrease the overall burden of pandemics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".